Using IRTree Models to Promote Selection Validity in the Presence of Extreme Response Styles.

ABSTRACT: The measurement of psychological constructs is frequently based on self-report tests, which often have Likert-type items rated from "Strongly Disagree" to "Strongly Agree". Recently, a family of item response theory (IRT) models called IRTree models have emerged that can parse out content traits (e.g., personality traits) from noise traits (e.g., response styles). In this study, we compare the selection validity and adverse impact consequences of noise traits on selection when scores are estimated using a generalized partial credit model (GPCM) or an IRTree model. First, we present a simulation which demonstrates that when noise traits do exist, the selection decisions made based on the IRTree model estimated scores have higher accuracy rates and have less instances of adverse impact based

SUBMITTER: Quirk VL 

PROVIDER: S-EPMC10672242 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature